Abstract
The current lack of a long, 30+ year, global climate data record of reflected shortwave top-of-atmosphere (TOA) radiation could be tackled by relying on existing narrowband records from the Advanced Very High Resolution Radiometer (AVHRR) instruments, and transform these measurements into broadband quantities like provided by the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents the methodology of an AVHRR-to-CERES narrowband-to-broadband conversion for shortwave TOA reflectance, including the ready-to-use results in the form of scene-type dependent regression coefficients, allowing a calculation of CERES-like shortwave broadband reflectance from AVHRR channels 1 and 2. The coefficients are obtained using empirical relations in a large data set of collocated, coangular and simultaneous AVHRR-CERES observations, requiring specific orbital conditions for the AVHRR- and CERES-carrying satellites, from which our data analysis uses all available data for an unprecedented observation matching between both instruments. The multivariate linear regressions were found to be robust and well-fitting, as demonstrated by the regression statistics on the calibration subset (80% of data): adjusted R 2 higher than 0.9 and relative RMS residual mostly below 3%, which is a significant improvement compared to previous regressions. Regression models are validated by applying them on a validation subset (20% of data), indicating a good performance overall, roughly similar to the calibration subset, and a negligible mean bias. A second validation approach uses an expanded data set with global coverage, allowing regional analyses. In the error analysis, instantaneous accuracy is quantified at regional scale between 1.8 Wm − 2 and 2.3 Wm − 2 (resp. clear-sky and overcast conditions) at 1 standard deviation (RMS bias). On daily and monthly time scales, these errors correspond to 0.7 and 0.9 Wm − 2 , which is compliant with the GCOS requirement of 1 Wm − 2 .
Highlights
Broadband measurements of top-of-atmosphere (TOA) reflected solar flux and emitted thermal flux are essential climate variables of which a high-quality data record of satellite measurements with sufficient length (“Climate Data Record” or CDR) is needed by, among others, the climate modeling community, and preferably spanning several decades [1]
This study aims to establish robust relations between the long-term record of narrowband Advanced Very High Resolution Radiometer (AVHRR) measurements, and the relatively recent state-of-the-art broadband measurements from Clouds and the Earth’s Radiant Energy System (CERES): these results allow the generation of a global, long-term, broadband energy balance dataset, that would fit the needs of the climate modeling and monitoring community
This paper establishes conversions between narrowband and broadband reflectances observed by respectively the AVHRR and CERES instruments
Summary
Broadband measurements of top-of-atmosphere (TOA) reflected solar flux and emitted thermal flux are essential climate variables of which a high-quality data record of satellite measurements with sufficient length (“Climate Data Record” or CDR) is needed by, among others, the climate modeling community (for validation purposes), and preferably spanning several decades [1]. Apart from instrument-specific broadband measurement campaigns (e.g., CERES, 2000-present [2]) and regional datasets based on geostationary satellites (e.g., Meteosat-based, 1983–2015 [3]), to date no harmonized global CDR dating back several decades exists. An alternative method is to rely on existing global long-term CDRs of harmonized narrowband reflectance (FCDRs), e.g., from the Advanced Very High Resolution Radiometer (AVHRR) instrument [4,5], and transform these measurements into broadband quantities [6]. This study aims to establish robust relations between the long-term record of narrowband AVHRR measurements, and the relatively recent state-of-the-art broadband measurements from CERES: these results allow the generation of a global, long-term, broadband energy balance dataset, that would fit the needs of the climate modeling and monitoring community. We focus on the first, empirical method, for which there are basically three possibilities of obtaining matched NTB pairs:
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